class_docstr: Side-Aware Boundary Localization (SABL) for RoI-Head.
  
  Side-Aware features are extracted by conv layers
  with an attention mechanism.
  Boundary Localization with Bucketing and Bucketing Guided Rescoring
  are implemented in BucketingBBoxCoder.
  
  Please refer to https://arxiv.org/abs/1912.04260 for more details.
  
  Args:
      cls_in_channels (int): Input channels of cls RoI feature.             Defaults to 256.
      reg_in_channels (int): Input channels of reg RoI feature.             Defaults to 256.
      roi_feat_size (int): Size of RoI features. Defaults to 7.
      reg_feat_up_ratio (int): Upsample ratio of reg features.             Defaults to 2.
      reg_pre_kernel (int): Kernel of 2D conv layers before             attention pooling. Defaults to 3.
      reg_post_kernel (int): Kernel of 1D conv layers after             attention pooling. Defaults to 3.
      reg_pre_num (int): Number of pre convs. Defaults to 2.
      reg_post_num (int): Number of post convs. Defaults to 1.
      num_classes (int): Number of classes in dataset. Defaults to 80.
      cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024.
      reg_offset_out_channels (int): Hidden and output channel             of reg offset branch. Defaults to 256.
      reg_cls_out_channels (int): Hidden and output channel             of reg cls branch. Defaults to 256.
      num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1.
      num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0.
      reg_class_agnostic (bool): Class agnostic regresion or not.             Defaults to True.
      norm_cfg (dict): Config of norm layers. Defaults to None.
      bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'.
      loss_cls (dict): Config of classification loss.
      loss_bbox_cls (dict): Config of classification loss for bbox branch.
      loss_bbox_reg (dict): Config of regression loss for bbox branch.
docstr: Predict bucketing esimation (cls_pred) and fine regression (offset
  pred) with side-aware features.
docstr_tok: ["Predict","bucketing","esimation","(","cls_pred",")","and","fine","regression","(","offset","pred",")","with","side-aware","features","."]
code: def reg_pred(self, x, offfset_fcs, cls_fcs):
      """Predict bucketing esimation (cls_pred) and fine regression (offset
      pred) with side-aware features."""
      x_offset = x.view(-1, self.reg_in_channels)
      x_cls = x.view(-1, self.reg_in_channels)
  
      for fc in offfset_fcs:
          x_offset = self.relu(fc(x_offset))
      for fc in cls_fcs:
          x_cls = self.relu(fc(x_cls))
      offset_pred = self.fc_reg_offset(x_offset)
      cls_pred = self.fc_reg_cls(x_cls)
  
      offset_pred = offset_pred.view(x.size(0), -1)
      cls_pred = cls_pred.view(x.size(0), -1)
  
      return offset_pred, cls_pred
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, kaiming_init, normal_init, xavier_init
  from mmcv.runner import force_fp32
  from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
  from mmdet.models.builder import HEADS, build_loss
  from mmdet.models.losses import accuracy
label_desc: Use image node_id 7 for calling the SABLHead obj's underlying member method code with example usage: obj.reg_pred(x, offfset_fcs, cls_fcs) and returns: offset_pred,  cls_pred